P = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Peptidi/154 variabili/101_peptidi-PreProcessed-IM-Step1-Step2-Step4-Step5-101.txt")
sum(is.na(P))
## [1] 1603792
the numbers of na is substantial
vis_miss(P,warn_large_data = FALSE)
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the visdat package.
## Please report the issue at <]8;;https://github.com/ropensci/visdat/issueshttps://github.com/ropensci/visdat/issues]8;;>.
the missing data is about 56%
skim(P)
| Name | P |
| Number of rows | 18290 |
| Number of columns | 154 |
| _______________________ | |
| Column type frequency: | |
| numeric | 154 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| X703.401372865699 | 14526 | 0.21 | 1.79 | 0.57 | 0.34 | 1.39 | 1.73 | 2.15 | 4.22 | ▂▇▅▁▁ |
| X704.39762274031 | 11096 | 0.39 | 1.91 | 0.60 | 0.28 | 1.48 | 1.86 | 2.30 | 4.70 | ▂▇▅▁▁ |
| X705.384775176124 | 3685 | 0.80 | 10.73 | 9.14 | 0.24 | 3.78 | 7.26 | 15.25 | 50.30 | ▇▃▂▁▁ |
| X706.388721051968 | 8345 | 0.54 | 4.69 | 3.04 | 0.38 | 2.40 | 3.66 | 6.22 | 16.87 | ▇▅▂▁▁ |
| X721.385792389616 | 113 | 0.99 | 19.52 | 19.46 | 0.26 | 4.92 | 11.87 | 28.30 | 105.23 | ▇▂▁▁▁ |
| X722.388097317932 | 4233 | 0.77 | 7.45 | 6.94 | 0.31 | 2.50 | 4.72 | 10.07 | 37.51 | ▇▂▁▁▁ |
| X726.439655161571 | 7652 | 0.58 | 24.48 | 20.91 | 0.31 | 8.71 | 17.00 | 34.38 | 139.30 | ▇▂▁▁▁ |
| X738.404675559703 | 10718 | 0.41 | 3.19 | 1.25 | 0.26 | 2.35 | 3.06 | 3.93 | 11.79 | ▅▇▁▁▁ |
| X739.41085100008 | 12131 | 0.34 | 2.02 | 0.69 | 0.15 | 1.55 | 1.96 | 2.43 | 5.72 | ▂▇▃▁▁ |
| X743.385993639694 | 2663 | 0.85 | 4.50 | 3.30 | 0.19 | 1.92 | 3.41 | 6.43 | 20.39 | ▇▃▂▁▁ |
| X764.397713644451 | 6922 | 0.62 | 3.33 | 1.83 | 0.14 | 1.93 | 2.80 | 4.40 | 10.75 | ▇▇▃▁▁ |
| X766.477627979316 | 1534 | 0.92 | 2.68 | 0.99 | 0.16 | 1.97 | 2.59 | 3.29 | 10.56 | ▅▇▁▁▁ |
| X771.438511022179 | 13724 | 0.25 | 1.99 | 0.76 | 0.11 | 1.42 | 1.91 | 2.46 | 5.28 | ▂▇▅▁▁ |
| X795.474297481607 | 2804 | 0.85 | 2.32 | 0.84 | 0.27 | 1.69 | 2.21 | 2.84 | 7.17 | ▃▇▂▁▁ |
| X796.463347662883 | 12714 | 0.30 | 1.86 | 0.53 | 0.24 | 1.48 | 1.83 | 2.22 | 3.64 | ▁▆▇▃▁ |
| X797.475093209898 | 10529 | 0.42 | 1.92 | 0.61 | 0.18 | 1.48 | 1.86 | 2.28 | 4.54 | ▁▇▆▁▁ |
| X811.465860310989 | 7815 | 0.57 | 8.07 | 4.96 | 0.23 | 4.13 | 6.78 | 10.99 | 29.01 | ▇▆▃▁▁ |
| X812.462812396293 | 7692 | 0.58 | 4.21 | 2.48 | 0.14 | 2.27 | 3.60 | 5.71 | 14.21 | ▇▇▃▁▁ |
| X813.519064384011 | 10869 | 0.41 | 2.54 | 0.95 | 0.16 | 1.91 | 2.46 | 3.02 | 10.95 | ▆▇▁▁▁ |
| X816.472991849074 | 7581 | 0.59 | 2.77 | 0.94 | 0.16 | 2.10 | 2.74 | 3.41 | 7.56 | ▂▇▅▁▁ |
| X817.420770454515 | 11493 | 0.37 | 2.07 | 0.73 | 0.31 | 1.52 | 1.99 | 2.52 | 9.10 | ▇▆▁▁▁ |
| X818.43774134942 | 12785 | 0.30 | 1.44 | 0.50 | 0.22 | 1.09 | 1.39 | 1.76 | 5.17 | ▅▇▁▁▁ |
| X833.121888423167 | 12797 | 0.30 | 7.97 | 3.72 | 1.07 | 5.59 | 7.73 | 9.77 | 49.17 | ▇▂▁▁▁ |
| X839.132555143816 | 10988 | 0.40 | 5.01 | 1.68 | 1.30 | 4.08 | 4.78 | 5.55 | 28.22 | ▇▁▁▁▁ |
| X840.140934584387 | 13660 | 0.25 | 2.84 | 1.05 | 0.74 | 2.31 | 2.67 | 3.08 | 16.54 | ▇▁▁▁▁ |
| X841.129972137687 | 14242 | 0.22 | 1.83 | 1.11 | 0.18 | 1.31 | 1.66 | 1.99 | 28.23 | ▇▁▁▁▁ |
| X842.550729203612 | 9673 | 0.47 | 21.18 | 16.58 | 1.41 | 9.96 | 16.91 | 27.37 | 210.52 | ▇▁▁▁▁ |
| X843.552178547911 | 8940 | 0.51 | 10.29 | 6.04 | 0.91 | 5.97 | 9.11 | 13.15 | 70.12 | ▇▂▁▁▁ |
| X844.543168992869 | 8010 | 0.56 | 3.59 | 2.06 | 0.37 | 2.17 | 3.17 | 4.46 | 26.27 | ▇▁▁▁▁ |
| X855.100759090494 | 8216 | 0.55 | 14.52 | 4.84 | 1.30 | 11.21 | 13.94 | 17.07 | 60.01 | ▆▇▁▁▁ |
| X856.103634808692 | 8074 | 0.56 | 6.43 | 2.11 | 0.91 | 4.99 | 6.16 | 7.53 | 27.00 | ▇▇▁▁▁ |
| X857.103870843625 | 13083 | 0.28 | 2.76 | 0.85 | 0.76 | 2.21 | 2.65 | 3.13 | 11.27 | ▇▅▁▁▁ |
| X859.544439470389 | 11332 | 0.38 | 22.93 | 18.04 | 0.46 | 8.59 | 18.53 | 32.72 | 107.98 | ▇▅▂▁▁ |
| X860.532361434122 | 8410 | 0.54 | 8.53 | 7.24 | 0.34 | 3.23 | 6.10 | 11.62 | 47.71 | ▇▂▁▁▁ |
| X871.076852631064 | 12276 | 0.33 | 4.11 | 1.55 | 1.02 | 3.07 | 3.85 | 4.82 | 16.17 | ▇▆▁▁▁ |
| X872.078608509269 | 14095 | 0.23 | 1.99 | 0.71 | 0.33 | 1.55 | 1.89 | 2.29 | 7.29 | ▅▇▁▁▁ |
| X873.547616791839 | 7290 | 0.60 | 5.08 | 4.16 | 0.18 | 1.97 | 3.70 | 6.97 | 26.47 | ▇▃▁▁▁ |
| X874.538835593762 | 8075 | 0.56 | 2.80 | 1.84 | 0.25 | 1.40 | 2.24 | 3.74 | 12.24 | ▇▅▂▁▁ |
| X877.098035665664 | 12588 | 0.31 | 5.13 | 2.54 | 0.69 | 3.44 | 4.65 | 6.27 | 48.46 | ▇▁▁▁▁ |
| X878.099308962539 | 14472 | 0.21 | 2.36 | 1.23 | 0.23 | 1.55 | 2.07 | 2.89 | 20.73 | ▇▁▁▁▁ |
| X881.52287297445 | 8765 | 0.52 | 3.78 | 2.45 | 0.11 | 1.93 | 3.11 | 5.04 | 17.61 | ▇▅▁▁▁ |
| X899.512111937031 | 769 | 0.96 | 2.14 | 0.83 | 0.19 | 1.54 | 2.01 | 2.65 | 6.03 | ▂▇▃▁▁ |
| X900.519132680702 | 6240 | 0.66 | 1.55 | 0.54 | 0.27 | 1.15 | 1.48 | 1.89 | 3.95 | ▂▇▅▁▁ |
| X901.534339846564 | 7782 | 0.57 | 1.36 | 0.49 | 0.26 | 1.01 | 1.30 | 1.65 | 5.52 | ▇▇▁▁▁ |
| X913.486722997486 | 13637 | 0.25 | 2.04 | 2.72 | 0.32 | 1.19 | 1.51 | 1.89 | 46.21 | ▇▁▁▁▁ |
| X919.505647165412 | 14382 | 0.21 | 1.00 | 0.30 | 0.15 | 0.78 | 0.96 | 1.20 | 2.11 | ▁▇▇▂▁ |
| X929.552854874804 | 8384 | 0.54 | 1.18 | 0.38 | 0.24 | 0.91 | 1.14 | 1.41 | 3.36 | ▃▇▂▁▁ |
| X930.570286785265 | 8013 | 0.56 | 1.21 | 0.65 | 0.15 | 0.80 | 1.05 | 1.42 | 6.22 | ▇▂▁▁▁ |
| X944.531705809784 | 345 | 0.98 | 4.92 | 5.20 | 0.23 | 1.85 | 2.92 | 5.67 | 36.76 | ▇▁▁▁▁ |
| X945.538617455054 | 9510 | 0.48 | 3.36 | 2.61 | 0.26 | 1.48 | 2.46 | 4.39 | 16.87 | ▇▃▁▁▁ |
| X951.534369302263 | 14436 | 0.21 | 1.39 | 0.47 | 0.09 | 1.03 | 1.35 | 1.72 | 3.29 | ▁▇▇▂▁ |
| X966.520332432519 | 13523 | 0.26 | 1.86 | 0.78 | 0.17 | 1.29 | 1.75 | 2.31 | 4.95 | ▃▇▅▁▁ |
| X968.539465096924 | 1469 | 0.92 | 2.28 | 0.98 | 0.16 | 1.56 | 2.09 | 2.85 | 9.16 | ▇▇▂▁▁ |
| X969.543037542197 | 5259 | 0.71 | 1.52 | 0.62 | 0.09 | 1.05 | 1.41 | 1.92 | 4.73 | ▃▇▃▁▁ |
| X982.518846084674 | 13895 | 0.24 | 1.56 | 0.64 | 0.08 | 1.07 | 1.47 | 1.97 | 3.96 | ▂▇▆▂▁ |
| X988.547508384061 | 11392 | 0.38 | 1.09 | 0.36 | 0.09 | 0.82 | 1.04 | 1.32 | 2.82 | ▁▇▅▁▁ |
| X989.54010791306 | 12888 | 0.30 | 1.04 | 0.34 | 0.07 | 0.78 | 1.00 | 1.25 | 2.41 | ▁▇▆▂▁ |
| X1021.56290721623 | 14408 | 0.21 | 1.03 | 0.36 | 0.17 | 0.78 | 0.98 | 1.25 | 2.54 | ▂▇▅▁▁ |
| X1023.52260855018 | 9037 | 0.51 | 1.26 | 0.45 | 0.14 | 0.94 | 1.20 | 1.50 | 5.08 | ▇▇▁▁▁ |
| X1024.54808802905 | 10395 | 0.43 | 1.22 | 0.66 | 0.17 | 0.82 | 1.08 | 1.42 | 7.67 | ▇▁▁▁▁ |
| X1025.56691773401 | 3021 | 0.83 | 2.64 | 1.05 | 0.14 | 1.87 | 2.48 | 3.29 | 8.70 | ▃▇▂▁▁ |
| X1026.57088472708 | 12438 | 0.32 | 1.80 | 0.68 | 0.10 | 1.28 | 1.73 | 2.25 | 4.71 | ▂▇▆▁▁ |
| X1044.14754874948 | 13875 | 0.24 | 3.41 | 1.44 | 1.13 | 2.78 | 3.27 | 3.78 | 30.42 | ▇▁▁▁▁ |
| X1045.55388620346 | 6010 | 0.67 | 3.54 | 2.13 | 0.34 | 2.18 | 2.97 | 4.29 | 24.38 | ▇▁▁▁▁ |
| X1046.569250586 | 4931 | 0.73 | 5.05 | 3.13 | 0.34 | 2.82 | 4.31 | 6.59 | 25.71 | ▇▃▁▁▁ |
| X1047.58953545812 | 12328 | 0.33 | 4.00 | 1.72 | 0.17 | 2.82 | 3.80 | 4.96 | 13.59 | ▃▇▂▁▁ |
| X1066.13228395 | 10253 | 0.44 | 4.56 | 1.91 | 0.25 | 3.25 | 4.44 | 5.61 | 23.75 | ▇▅▁▁▁ |
| X1067.138669068 | 13449 | 0.26 | 3.04 | 1.12 | 0.64 | 2.34 | 2.95 | 3.57 | 13.21 | ▇▅▁▁▁ |
| X1067.57717570472 | 10623 | 0.42 | 6.80 | 5.58 | 0.14 | 2.93 | 4.87 | 8.94 | 45.13 | ▇▂▁▁▁ |
| X1068.57935749796 | 9900 | 0.46 | 4.25 | 3.15 | 0.27 | 2.03 | 3.24 | 5.52 | 25.00 | ▇▂▁▁▁ |
| X1081.58520829662 | 6992 | 0.62 | 4.90 | 4.43 | 0.12 | 1.76 | 3.22 | 6.72 | 29.84 | ▇▂▁▁▁ |
| X1082.11020311348 | 10184 | 0.44 | 2.47 | 1.09 | 0.32 | 1.71 | 2.40 | 3.02 | 13.34 | ▇▃▁▁▁ |
| X1082.59678591629 | 6539 | 0.64 | 3.52 | 2.62 | 0.15 | 1.64 | 2.68 | 4.62 | 18.81 | ▇▃▁▁▁ |
| X1083.11177342918 | 14341 | 0.22 | 1.57 | 0.66 | 0.25 | 1.19 | 1.52 | 1.84 | 6.91 | ▇▆▁▁▁ |
| X1083.59374890608 | 11292 | 0.38 | 1.84 | 0.91 | 0.15 | 1.22 | 1.68 | 2.24 | 7.81 | ▇▇▁▁▁ |
| X1099.62441990754 | 6355 | 0.65 | 5.21 | 6.16 | 0.17 | 1.60 | 2.93 | 6.44 | 73.29 | ▇▁▁▁▁ |
| X1100.61311177393 | 5431 | 0.70 | 9.38 | 11.61 | 0.24 | 2.28 | 4.76 | 11.74 | 121.79 | ▇▁▁▁▁ |
| X1101.61429665547 | 6473 | 0.65 | 4.78 | 5.61 | 0.10 | 1.31 | 2.52 | 6.03 | 56.85 | ▇▁▁▁▁ |
| X1131.62829179685 | 4453 | 0.76 | 6.13 | 6.23 | 0.16 | 1.50 | 3.69 | 8.78 | 39.99 | ▇▂▁▁▁ |
| X1132.6208985625 | 5516 | 0.70 | 4.17 | 3.84 | 0.16 | 1.33 | 2.74 | 5.85 | 24.80 | ▇▂▁▁▁ |
| X1133.62168504174 | 9127 | 0.50 | 2.11 | 1.36 | 0.10 | 1.10 | 1.70 | 2.78 | 9.04 | ▇▅▂▁▁ |
| X1153.61458557877 | 5009 | 0.73 | 5.02 | 4.36 | 0.09 | 1.65 | 3.34 | 7.37 | 26.51 | ▇▂▂▁▁ |
| X1154.61607341723 | 6682 | 0.63 | 3.60 | 2.71 | 0.09 | 1.48 | 2.68 | 5.19 | 16.53 | ▇▃▂▁▁ |
| X1155.62078684515 | 8020 | 0.56 | 1.55 | 0.90 | 0.14 | 0.84 | 1.31 | 2.07 | 5.56 | ▇▆▂▁▁ |
| X1169.60900885242 | 10565 | 0.42 | 3.52 | 2.17 | 0.13 | 1.74 | 3.12 | 4.99 | 15.09 | ▇▆▂▁▁ |
| X1170.60905838657 | 13564 | 0.26 | 2.24 | 1.35 | 0.16 | 1.10 | 2.00 | 3.18 | 6.85 | ▇▆▅▂▁ |
| X1171.61680180233 | 12116 | 0.34 | 1.12 | 0.49 | 0.10 | 0.74 | 1.03 | 1.42 | 3.11 | ▃▇▅▁▁ |
| X1198.7489884275 | 816 | 0.96 | 2.02 | 0.77 | 0.18 | 1.46 | 1.90 | 2.46 | 6.91 | ▃▇▂▁▁ |
| X1199.74616711023 | 3130 | 0.83 | 1.60 | 0.58 | 0.17 | 1.18 | 1.51 | 1.94 | 5.20 | ▃▇▂▁▁ |
| X1200.72216225828 | 5657 | 0.69 | 0.86 | 0.31 | 0.07 | 0.64 | 0.82 | 1.05 | 2.37 | ▂▇▃▁▁ |
| X1221.67497921762 | 14554 | 0.20 | 0.71 | 0.23 | 0.13 | 0.54 | 0.68 | 0.85 | 1.59 | ▂▇▆▂▁ |
| X1237.70248667576 | 13669 | 0.25 | 0.86 | 0.28 | 0.13 | 0.65 | 0.83 | 1.04 | 2.09 | ▂▇▅▁▁ |
| X1241.6702211025 | 13459 | 0.26 | 0.70 | 0.25 | 0.11 | 0.52 | 0.68 | 0.87 | 2.05 | ▃▇▃▁▁ |
| X1242.67907139247 | 9262 | 0.49 | 0.67 | 0.23 | 0.12 | 0.49 | 0.64 | 0.82 | 1.72 | ▂▇▅▁▁ |
| X1255.66639225835 | 14347 | 0.22 | 0.73 | 0.25 | 0.13 | 0.54 | 0.70 | 0.90 | 1.75 | ▂▇▅▁▁ |
| X1268.68566515798 | 13416 | 0.27 | 0.63 | 0.21 | 0.09 | 0.47 | 0.60 | 0.76 | 1.83 | ▂▇▂▁▁ |
| X1287.7198274242 | 13036 | 0.29 | 0.96 | 0.54 | 0.09 | 0.58 | 0.83 | 1.18 | 4.21 | ▇▅▁▁▁ |
| X1300.69101239099 | 10185 | 0.44 | 0.60 | 0.20 | 0.07 | 0.45 | 0.57 | 0.72 | 1.59 | ▂▇▃▁▁ |
| X1318.7055011406 | 12921 | 0.29 | 0.84 | 0.29 | 0.14 | 0.62 | 0.81 | 1.02 | 2.18 | ▂▇▅▁▁ |
| X1336.69651172215 | 2459 | 0.87 | 4.73 | 5.63 | 0.13 | 1.02 | 2.33 | 6.35 | 43.04 | ▇▁▁▁▁ |
| X1337.6996697515 | 6373 | 0.65 | 3.85 | 4.01 | 0.14 | 1.08 | 2.23 | 5.25 | 28.97 | ▇▂▁▁▁ |
| X1338.69026337081 | 6703 | 0.63 | 2.19 | 1.78 | 0.11 | 0.97 | 1.58 | 2.79 | 13.57 | ▇▂▁▁▁ |
| X1352.69068322102 | 2087 | 0.89 | 5.59 | 6.53 | 0.09 | 1.17 | 2.85 | 7.55 | 45.04 | ▇▂▁▁▁ |
| X1353.70217721775 | 9101 | 0.50 | 2.81 | 3.58 | 0.09 | 0.82 | 1.53 | 3.15 | 30.61 | ▇▁▁▁▁ |
| X1354.69509872675 | 11358 | 0.38 | 1.84 | 1.47 | 0.13 | 0.94 | 1.39 | 2.17 | 14.70 | ▇▁▁▁▁ |
| X1359.71920064559 | 13132 | 0.28 | 1.21 | 0.61 | 0.11 | 0.74 | 1.10 | 1.57 | 4.21 | ▇▇▃▁▁ |
| X1374.68868544969 | 5609 | 0.69 | 1.71 | 1.30 | 0.11 | 0.75 | 1.24 | 2.34 | 9.91 | ▇▂▁▁▁ |
| X1375.70367765744 | 9157 | 0.50 | 1.34 | 0.87 | 0.11 | 0.68 | 1.05 | 1.77 | 7.28 | ▇▃▁▁▁ |
| X1376.70057890047 | 9575 | 0.48 | 0.96 | 0.48 | 0.10 | 0.59 | 0.87 | 1.24 | 3.60 | ▇▇▂▁▁ |
| X1391.7310555169 | 13995 | 0.23 | 0.68 | 0.28 | 0.11 | 0.48 | 0.63 | 0.81 | 2.28 | ▆▇▂▁▁ |
| X1393.72199322531 | 13802 | 0.25 | 0.65 | 0.23 | 0.08 | 0.48 | 0.62 | 0.80 | 1.57 | ▂▇▅▂▁ |
| X1396.732964339 | 14244 | 0.22 | 0.72 | 0.26 | 0.13 | 0.52 | 0.70 | 0.88 | 1.96 | ▃▇▃▁▁ |
| X1397.74684785689 | 13563 | 0.26 | 0.61 | 0.21 | 0.09 | 0.45 | 0.60 | 0.75 | 1.56 | ▂▇▅▁▁ |
| X1445.76205363255 | 13373 | 0.27 | 0.50 | 0.18 | 0.09 | 0.38 | 0.48 | 0.59 | 2.11 | ▇▆▁▁▁ |
| X1446.76977484297 | 13444 | 0.26 | 0.53 | 0.18 | 0.07 | 0.40 | 0.51 | 0.64 | 1.83 | ▅▇▁▁▁ |
| X1460.77544240007 | 13099 | 0.28 | 2.98 | 2.06 | 0.06 | 1.20 | 2.67 | 4.34 | 13.50 | ▇▆▂▁▁ |
| X1462.78675966734 | 14211 | 0.22 | 1.31 | 0.71 | 0.06 | 0.74 | 1.20 | 1.74 | 4.52 | ▇▇▃▁▁ |
| X1467.80248378337 | 13794 | 0.25 | 0.59 | 0.25 | 0.07 | 0.40 | 0.55 | 0.73 | 1.74 | ▅▇▃▁▁ |
| X1468.79545331291 | 13959 | 0.24 | 0.50 | 0.21 | 0.06 | 0.34 | 0.45 | 0.62 | 1.72 | ▆▇▂▁▁ |
| X1482.7828636732 | 10174 | 0.44 | 1.62 | 0.98 | 0.09 | 0.86 | 1.39 | 2.20 | 6.98 | ▇▅▂▁▁ |
| X1483.7602040513 | 10438 | 0.43 | 1.10 | 0.65 | 0.11 | 0.61 | 0.92 | 1.43 | 4.91 | ▇▅▁▁▁ |
| X1490.79921206715 | 14061 | 0.23 | 0.45 | 0.16 | 0.05 | 0.33 | 0.42 | 0.54 | 1.39 | ▃▇▂▁▁ |
| X1505.81814466814 | 11371 | 0.38 | 0.54 | 0.20 | 0.08 | 0.40 | 0.52 | 0.67 | 1.59 | ▃▇▃▁▁ |
| X1506.82927677072 | 12647 | 0.31 | 0.69 | 0.28 | 0.08 | 0.47 | 0.66 | 0.88 | 1.94 | ▃▇▅▁▁ |
| X1520.76684236019 | 10519 | 0.42 | 0.66 | 0.30 | 0.09 | 0.43 | 0.61 | 0.85 | 2.15 | ▆▇▃▁▁ |
| X1548.79708685808 | 12093 | 0.34 | 0.51 | 0.17 | 0.09 | 0.39 | 0.49 | 0.61 | 1.29 | ▂▇▃▁▁ |
| X1549.80251865168 | 13769 | 0.25 | 0.45 | 0.15 | 0.07 | 0.34 | 0.43 | 0.54 | 1.05 | ▁▇▅▁▁ |
| X1567.81408356388 | 12086 | 0.34 | 1.53 | 1.47 | 0.09 | 0.60 | 1.05 | 1.91 | 20.13 | ▇▁▁▁▁ |
| X1570.82757902878 | 13451 | 0.26 | 0.85 | 0.51 | 0.10 | 0.48 | 0.72 | 1.08 | 4.18 | ▇▃▁▁▁ |
| X1587.83475034977 | 14150 | 0.23 | 0.40 | 0.14 | 0.08 | 0.30 | 0.38 | 0.49 | 1.38 | ▆▇▂▁▁ |
| X1588.84025634597 | 13937 | 0.24 | 0.38 | 0.13 | 0.06 | 0.29 | 0.37 | 0.47 | 0.93 | ▂▇▅▁▁ |
| X1617.8705321642 | 14048 | 0.23 | 0.40 | 0.13 | 0.06 | 0.30 | 0.38 | 0.48 | 1.22 | ▃▇▂▁▁ |
| X1628.84115733793 | 13328 | 0.27 | 0.47 | 0.18 | 0.08 | 0.34 | 0.44 | 0.57 | 1.52 | ▅▇▂▁▁ |
| X1629.84658199452 | 14273 | 0.22 | 0.42 | 0.16 | 0.07 | 0.31 | 0.39 | 0.51 | 1.53 | ▇▇▂▁▁ |
| X1643.86831200385 | 14268 | 0.22 | 0.36 | 0.12 | 0.08 | 0.27 | 0.34 | 0.43 | 1.03 | ▃▇▂▁▁ |
| X1676.89152948382 | 13388 | 0.27 | 0.34 | 0.11 | 0.07 | 0.26 | 0.32 | 0.40 | 0.94 | ▃▇▂▁▁ |
| X1677.8916781126 | 13927 | 0.24 | 0.31 | 0.11 | 0.03 | 0.23 | 0.29 | 0.37 | 0.79 | ▁▇▅▁▁ |
| X1708.89992075846 | 13692 | 0.25 | 0.43 | 0.17 | 0.09 | 0.31 | 0.40 | 0.52 | 1.43 | ▆▇▂▁▁ |
| X1724.90873224483 | 14075 | 0.23 | 0.70 | 0.43 | 0.06 | 0.39 | 0.57 | 0.90 | 3.52 | ▇▃▁▁▁ |
| X1725.90758432642 | 14565 | 0.20 | 0.50 | 0.31 | 0.07 | 0.29 | 0.41 | 0.63 | 2.84 | ▇▂▁▁▁ |
| X1754.93218998318 | 14471 | 0.21 | 0.35 | 0.14 | 0.05 | 0.24 | 0.33 | 0.42 | 1.04 | ▃▇▂▁▁ |
| X1812.96278802887 | 14533 | 0.21 | 0.28 | 0.10 | 0.06 | 0.21 | 0.26 | 0.33 | 0.84 | ▃▇▂▁▁ |
| X1907.98379041504 | 14566 | 0.20 | 0.18 | 0.06 | 0.02 | 0.14 | 0.18 | 0.22 | 0.47 | ▂▇▅▁▁ |
| X2032.06680023288 | 13888 | 0.24 | 0.18 | 0.06 | 0.03 | 0.14 | 0.17 | 0.21 | 0.45 | ▂▇▅▁▁ |
| X2033.10347402711 | 13164 | 0.28 | 0.18 | 0.07 | 0.01 | 0.14 | 0.18 | 0.22 | 0.57 | ▂▇▂▁▁ |
| X2055.07059438912 | 13944 | 0.24 | 0.16 | 0.05 | 0.02 | 0.12 | 0.15 | 0.19 | 0.41 | ▂▇▅▁▁ |
| X2106.08527885176 | 13936 | 0.24 | 0.14 | 0.05 | 0.00 | 0.10 | 0.13 | 0.17 | 0.37 | ▂▇▅▁▁ |
| X2128.13844472021 | 14591 | 0.20 | 0.16 | 0.06 | 0.01 | 0.12 | 0.15 | 0.19 | 0.39 | ▂▇▅▁▁ |
| X2138.11220461466 | 10229 | 0.44 | 0.16 | 0.06 | 0.01 | 0.12 | 0.15 | 0.20 | 0.54 | ▃▇▂▁▁ |
| X2166.14516832135 | 12139 | 0.34 | 0.23 | 0.15 | 0.01 | 0.13 | 0.19 | 0.30 | 1.20 | ▇▃▁▁▁ |
| X2400.27542672266 | 12868 | 0.30 | 0.11 | 0.05 | 0.00 | 0.07 | 0.10 | 0.14 | 0.41 | ▅▇▂▁▁ |
| X2401.27062857394 | 14294 | 0.22 | 0.10 | 0.04 | 0.00 | 0.07 | 0.10 | 0.13 | 0.30 | ▃▇▃▁▁ |
| X2403.28670488561 | 13332 | 0.27 | 0.16 | 0.09 | 0.01 | 0.10 | 0.14 | 0.20 | 0.74 | ▇▅▁▁▁ |
| X2404.29130915879 | 14226 | 0.22 | 0.16 | 0.09 | 0.01 | 0.10 | 0.14 | 0.20 | 0.78 | ▇▅▁▁▁ |
we observe that the missing data is not uniform in the mz, there are some values for which only 20 - 30% of the pixel have a value, and this tends to be small, this is specially true in the for the large values of mz in this case
we replace the missing data with 0 since it means the data for that mz was under threshold
P0 = P
P0[is.na(P0)] = 0
cm <- cor(P0)
corrplot(cm, method = "color", tl.pos = 'n')
we can see a correlation between the different mz values in blocks, we have the highest mz that seem o be unncorrelate to everithing else
pixels = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Peptidi/154 variabili/101_peptidi-PreProcessed-XYCoordinates-Step1-Step2-Step4-Step5-101.txt")
colnames(P0) = substr(colnames(P0),1,5)
colnames(pixels) = c("x","y")
max_n_of_pixel = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Peptidi/154 variabili/101_peptidi-PreProcessed-maxXY-Step1-Step2-Step4-Step5-101.txt")
Data_long = as_tibble(data.frame( pixels, P0 ))
max_number_of_pixels = apply(Data_long[,1:2],2,max)
Data_array = matrix(NA,max_number_of_pixels[1],max_number_of_pixels[2])
Data_array = array(NA,c(max_number_of_pixels[1],max_number_of_pixels[2],ncol(P0)))
# there must be a better way to do this
for(k in 1:ncol(P0)){
for(i in 1:nrow(Data_long)){
Data_array[Data_long$x[i],Data_long$y[i],k] = P0[i,k]
}
}
dim(Data_array)
## x y
## 157 178 154
Data_very_long = reshape2::melt(Data_long,c("x","y")) %>% mutate(pixel_ind = paste0(x,"_",y), value_ind = rep(1:nrow(Data_long),ncol(P0)))
Data_very_long = Data_very_long %>% group_by(pixel_ind) %>% mutate(n = row_number()) %>% ungroup() %>% mutate(mz = as.numeric(substr(variable,2,5)))
Data_very_long = reshape2::melt(Data_long,c("x","y")) %>% mutate(pixel_ind = paste0(x,"_",y), value_ind = rep(1:nrow(Data_long),ncol(P0)))
Data_very_long = Data_very_long %>% group_by(pixel_ind) %>% mutate(n = row_number()) %>% ungroup() %>% mutate(mz = as.numeric(substr(variable,2,5)))
# subsampling to get a faster plot and not drain memory
sub_ind = sample(unique(Data_very_long$pixel_ind),1000)
# just to get the gist:
ggplot(Data_very_long %>% filter(pixel_ind %in% sub_ind))+
geom_path(aes(x = mz, y = value,
col=pixel_ind,
group = pixel_ind),alpha=.5)+theme_bw()+theme(legend.position = "none")+xlab("m.z")+scale_color_viridis_d(option = "A")+
scale_x_continuous(n.breaks = 20)
mz_values <- colnames(P0)
here we can see the first peaks that show a distinctive shape
this show the same pattern as before
still the same pattern the peak is at 811
we have the same spots as in the glicani
we still have this spots and some edge activations, possible biological meaning?
same patterns
complementary to the main pattern, not too high of spikes
same patterns
same patterns
we have some spots that seem to be outlier, and the rest is just the same structure
same pattern
same patter
the rest is just noise and mow values of the same structure
pca = princomp(P0)
plot(pca)
summary(pca)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 40.3991506 15.9743645 8.13712111 7.29852391 5.84229587
## Proportion of Variance 0.7289469 0.1139721 0.02957284 0.02379148 0.01524469
## Cumulative Proportion 0.7289469 0.8429190 0.87249188 0.89628337 0.91152806
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 5.3612942 5.06501681 4.99080393 3.258097082 2.915194794
## Proportion of Variance 0.0128378 0.01145812 0.01112481 0.004741104 0.003795654
## Cumulative Proportion 0.9243659 0.93582397 0.94694878 0.951689885 0.955485539
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 2.733224687 2.672351245 2.369943632 2.276792634
## Proportion of Variance 0.003336584 0.003189617 0.002508577 0.002315253
## Cumulative Proportion 0.958822123 0.962011740 0.964520317 0.966835570
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 2.045413443 1.94957808 1.917887220 1.760352181
## Proportion of Variance 0.001868588 0.00169759 0.001642849 0.001384046
## Cumulative Proportion 0.968704158 0.97040175 0.972044597 0.973428643
## Comp.19 Comp.20 Comp.21 Comp.22
## Standard deviation 1.735462414 1.604566753 1.51780903 1.512897379
## Proportion of Variance 0.001345185 0.001149918 0.00102893 0.001022281
## Cumulative Proportion 0.974773827 0.975923746 0.97695268 0.977974957
## Comp.23 Comp.24 Comp.25 Comp.26
## Standard deviation 1.4779005303 1.4369422782 1.3526720350 1.3392595307
## Proportion of Variance 0.0009755327 0.0009222106 0.0008172153 0.0008010893
## Cumulative Proportion 0.9789504894 0.9798727000 0.9806899152 0.9814910045
## Comp.27 Comp.28 Comp.29 Comp.30
## Standard deviation 1.3301421399 1.3006730276 1.2638180367 1.2450017975
## Proportion of Variance 0.0007902191 0.0007555926 0.0007133794 0.0006922953
## Cumulative Proportion 0.9822812237 0.9830368163 0.9837501957 0.9844424910
## Comp.31 Comp.32 Comp.33 Comp.34
## Standard deviation 1.2196069006 1.1878905928 1.1674621780 1.1523563644
## Proportion of Variance 0.0006643412 0.0006302376 0.0006087473 0.0005930961
## Cumulative Proportion 0.9851068322 0.9857370698 0.9863458172 0.9869389132
## Comp.35 Comp.36 Comp.37 Comp.38
## Standard deviation 1.1096610004 1.0699072836 1.0542070594 1.0063699005
## Proportion of Variance 0.0005499612 0.0005112623 0.0004963674 0.0004523418
## Cumulative Proportion 0.9874888745 0.9880001367 0.9884965042 0.9889488460
## Comp.39 Comp.40 Comp.41 Comp.42
## Standard deviation 0.9853022068 0.9773389411 0.9610894117 0.9146349955
## Proportion of Variance 0.0004336011 0.0004266206 0.0004125523 0.0003736346
## Cumulative Proportion 0.9893824471 0.9898090677 0.9902216200 0.9905952546
## Comp.43 Comp.44 Comp.45 Comp.46
## Standard deviation 0.8894101221 0.8665821721 0.8556113679 0.8100176747
## Proportion of Variance 0.0003533097 0.0003354061 0.0003269675 0.0002930491
## Cumulative Proportion 0.9909485643 0.9912839704 0.9916109378 0.9919039870
## Comp.47 Comp.48 Comp.49 Comp.50
## Standard deviation 0.7816382686 0.7679075614 0.7618670460 0.7391997202
## Proportion of Variance 0.0002728746 0.0002633718 0.0002592447 0.0002440479
## Cumulative Proportion 0.9921768615 0.9924402334 0.9926994780 0.9929435259
## Comp.51 Comp.52 Comp.53 Comp.54
## Standard deviation 0.7294960438 0.7029509446 0.7015565015 0.685351777
## Proportion of Variance 0.0002376826 0.0002206996 0.0002198248 0.000209787
## Cumulative Proportion 0.9931812085 0.9934019080 0.9936217329 0.993831520
## Comp.55 Comp.56 Comp.57 Comp.58
## Standard deviation 0.6787912683 0.6682097030 0.648940398 0.6453566954
## Proportion of Variance 0.0002057898 0.0001994238 0.000188088 0.0001860163
## Cumulative Proportion 0.9940373097 0.9942367335 0.994424821 0.9946108378
## Comp.59 Comp.60 Comp.61 Comp.62
## Standard deviation 0.6336153480 0.6240019021 0.6072965336 0.6044566256
## Proportion of Variance 0.0001793093 0.0001739095 0.0001647225 0.0001631856
## Cumulative Proportion 0.9947901471 0.9949640566 0.9951287791 0.9952919647
## Comp.63 Comp.64 Comp.65 Comp.66
## Standard deviation 0.5992822507 0.5930071286 0.5862482479 0.5667055393
## Proportion of Variance 0.0001604037 0.0001570621 0.0001535022 0.0001434387
## Cumulative Proportion 0.9954523684 0.9956094304 0.9957629326 0.9959063713
## Comp.67 Comp.68 Comp.69 Comp.70
## Standard deviation 0.5565288927 0.5518703336 0.5466714688 0.5414292616
## Proportion of Variance 0.0001383333 0.0001360271 0.0001334763 0.0001309287
## Cumulative Proportion 0.9960447047 0.9961807318 0.9963142081 0.9964451368
## Comp.71 Comp.72 Comp.73 Comp.74
## Standard deviation 0.5245472376 0.519162362 0.5166308171 0.5086563119
## Proportion of Variance 0.0001228912 0.000120381 0.0001192098 0.0001155581
## Cumulative Proportion 0.9965680280 0.996688409 0.9968076188 0.9969231769
## Comp.75 Comp.76 Comp.77 Comp.78
## Standard deviation 0.5071022703 0.4945808853 0.4899254174 0.4877170331
## Proportion of Variance 0.0001148531 0.0001092512 0.0001072041 0.0001062398
## Cumulative Proportion 0.9970380299 0.9971472811 0.9972544852 0.9973607250
## Comp.79 Comp.80 Comp.81 Comp.82
## Standard deviation 0.4819257010 0.4744127957 0.4666743708 4.617112e-01
## Proportion of Variance 0.0001037317 0.0001005227 0.0000972701 9.521211e-05
## Cumulative Proportion 0.9974644567 0.9975649794 0.9976622495 9.977575e-01
## Comp.83 Comp.84 Comp.85 Comp.86
## Standard deviation 4.498147e-01 4.399089e-01 4.377205e-01 4.342643e-01
## Proportion of Variance 9.036884e-05 8.643247e-05 8.557465e-05 8.422862e-05
## Cumulative Proportion 9.978478e-01 9.979343e-01 9.980198e-01 9.981041e-01
## Comp.87 Comp.88 Comp.89 Comp.90
## Standard deviation 4.297765e-01 4.285246e-01 0.4239960143 4.070508e-01
## Proportion of Variance 8.249673e-05 8.201681e-05 0.0000802925 7.400287e-05
## Cumulative Proportion 9.981866e-01 9.982686e-01 0.9983488723 9.984229e-01
## Comp.91 Comp.92 Comp.93 Comp.94
## Standard deviation 4.052982e-01 3.962946e-01 3.900365e-01 3.785081e-01
## Proportion of Variance 7.336702e-05 7.014357e-05 6.794569e-05 6.398849e-05
## Cumulative Proportion 9.984962e-01 9.985664e-01 9.986343e-01 9.986983e-01
## Comp.95 Comp.96 Comp.97 Comp.98
## Standard deviation 3.731613e-01 3.334123e-01 3.272569e-01 0.3236794842
## Proportion of Variance 6.219346e-05 4.964947e-05 4.783316e-05 0.0000467931
## Cumulative Proportion 9.987605e-01 9.988102e-01 9.988580e-01 0.9989047891
## Comp.99 Comp.100 Comp.101 Comp.102
## Standard deviation 0.3169676141 3.145463e-01 3.077433e-01 3.072566e-01
## Proportion of Variance 0.0000448726 4.418966e-05 4.229885e-05 4.216517e-05
## Cumulative Proportion 0.9989496617 9.989939e-01 9.990362e-01 9.990783e-01
## Comp.103 Comp.104 Comp.105 Comp.106
## Standard deviation 3.031456e-01 3.005097e-01 2.978941e-01 2.935509e-01
## Proportion of Variance 4.104442e-05 4.033374e-05 3.963466e-05 3.848737e-05
## Cumulative Proportion 9.991194e-01 9.991597e-01 9.991993e-01 9.992378e-01
## Comp.107 Comp.108 Comp.109 Comp.110
## Standard deviation 2.887736e-01 2.877916e-01 2.843650e-01 0.2818193309
## Proportion of Variance 3.724487e-05 3.699199e-05 3.611635e-05 0.0000354726
## Cumulative Proportion 9.992751e-01 9.993121e-01 9.993482e-01 0.9993836414
## Comp.111 Comp.112 Comp.113 Comp.114
## Standard deviation 2.746143e-01 0.2711894496 0.2627414775 2.555900e-01
## Proportion of Variance 3.368198e-05 0.0000328471 0.0000308325 2.917691e-05
## Cumulative Proportion 9.994173e-01 0.9994501704 0.9994810029 9.995102e-01
## Comp.115 Comp.116 Comp.117 Comp.118
## Standard deviation 2.506924e-01 2.460048e-01 2.425202e-01 2.379811e-01
## Proportion of Variance 2.806944e-05 2.702953e-05 2.626922e-05 2.529509e-05
## Cumulative Proportion 9.995382e-01 9.995653e-01 9.995915e-01 9.996168e-01
## Comp.119 Comp.120 Comp.121 Comp.122
## Standard deviation 2.318558e-01 2.263664e-01 2.197474e-01 2.179916e-01
## Proportion of Variance 2.400973e-05 2.288629e-05 2.156747e-05 2.122418e-05
## Cumulative Proportion 9.996409e-01 9.996637e-01 9.996853e-01 9.997065e-01
## Comp.123 Comp.124 Comp.125 Comp.126
## Standard deviation 2.153974e-01 2.116471e-01 2.097317e-01 2.064405e-01
## Proportion of Variance 2.072204e-05 2.000673e-05 1.964624e-05 1.903449e-05
## Cumulative Proportion 9.997273e-01 9.997473e-01 9.997669e-01 9.997859e-01
## Comp.127 Comp.128 Comp.129 Comp.130
## Standard deviation 2.028313e-01 0.2000042102 0.1836679365 1.790292e-01
## Proportion of Variance 1.837474e-05 0.0000178661 0.0000150667 1.431525e-05
## Cumulative Proportion 9.998043e-01 0.9998221811 0.9998372478 9.998516e-01
## Comp.131 Comp.132 Comp.133 Comp.134
## Standard deviation 1.739702e-01 1.711473e-01 1.687172e-01 1.667773e-01
## Proportion of Variance 1.351764e-05 1.308253e-05 1.271364e-05 1.242297e-05
## Cumulative Proportion 9.998651e-01 9.998782e-01 9.998909e-01 9.999033e-01
## Comp.135 Comp.136 Comp.137 Comp.138
## Standard deviation 1.641567e-01 1.531942e-01 1.490205e-01 1.473419e-01
## Proportion of Variance 1.203563e-05 1.048181e-05 9.918439e-06 9.696248e-06
## Cumulative Proportion 9.999153e-01 9.999258e-01 9.999357e-01 9.999454e-01
## Comp.139 Comp.140 Comp.141 Comp.142
## Standard deviation 1.377268e-01 1.342370e-01 1.096922e-01 1.087324e-01
## Proportion of Variance 8.472046e-06 8.048144e-06 5.374063e-06 5.280429e-06
## Cumulative Proportion 9.999539e-01 9.999620e-01 9.999673e-01 9.999726e-01
## Comp.143 Comp.144 Comp.145 Comp.146
## Standard deviation 1.068803e-01 8.395213e-02 8.043323e-02 7.652334e-02
## Proportion of Variance 5.102076e-06 3.147856e-06 2.889498e-06 2.615407e-06
## Cumulative Proportion 9.999777e-01 9.999809e-01 9.999837e-01 9.999864e-01
## Comp.147 Comp.148 Comp.149 Comp.150
## Standard deviation 7.579695e-02 7.097042e-02 6.949090e-02 6.823466e-02
## Proportion of Variance 2.565990e-06 2.249605e-06 2.156787e-06 2.079513e-06
## Cumulative Proportion 9.999889e-01 9.999912e-01 9.999933e-01 9.999954e-01
## Comp.151 Comp.152 Comp.153 Comp.154
## Standard deviation 5.967253e-02 5.619498e-02 4.468675e-02 3.941605e-02
## Proportion of Variance 1.590378e-06 1.410414e-06 8.918851e-07 6.939011e-07
## Cumulative Proportion 9.999970e-01 9.999984e-01 9.999993e-01 1.000000e+00
the pca works well we have 92% explane d variance with 6 components
PCA1 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,1]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA2 = ggplot(Data_long %>% mutate(pca2 = pca$scores[,2]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA3 = ggplot(Data_long %>% mutate(pca3 = pca$scores[,3]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca3))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA4 = ggplot(Data_long %>% mutate(pca4 = pca$scores[4]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca4))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA5 = ggplot(Data_long %>% mutate(pca5 = pca$scores[,5]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca5))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA6 = ggplot(Data_long %>% mutate(pca6 = pca$scores[,6]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca6))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA1+PCA2+PCA3+PCA4+PCA5+PCA6
we can clearly see the main patterns in the data
PCA1 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,1]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA3 = ggplot(Data_long %>% mutate(pca3 = pca$scores[,3]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca3))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA5 = ggplot(Data_long %>% mutate(pca5 = pca$scores[,5]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca5))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA6 = ggplot(Data_long %>% mutate(pca6 = pca$scores[,6]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca6))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P1 = ggplot(Data_long)+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = X705.))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P2 = ggplot(Data_long)+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = X859.))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA1+PCA3+PCA5+PCA6+P1+P2
these are the conponentss of the main shape
PCA2 = ggplot(Data_long %>% mutate(pca2 = pca$scores[,2]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P2 = ggplot(Data_long)+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = X842.))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA2+P2
as expected is just 842